RDD.reduceByKey(func: Callable[[V, V], V], numPartitions: Optional[int] = None, partitionFunc: Callable[[K], int] = <function portable_hash>) → pyspark.rdd.RDD[Tuple[K, V]][source]

Merge the values for each key using an associative and commutative reduce function.

This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a “combiner” in MapReduce.

Output will be partitioned with numPartitions partitions, or the default parallelism level if numPartitions is not specified. Default partitioner is hash-partition.

New in version 1.6.0.


the reduce function

numPartitionsint, optional

the number of partitions in new RDD

partitionFuncfunction, optional, default portable_hash

function to compute the partition index


a RDD containing the keys and the aggregated result for each key


>>> from operator import add
>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
>>> sorted(rdd.reduceByKey(add).collect())
[('a', 2), ('b', 1)]